Cytoplasmic RNA-Protein Particles Exhibit Non-Gaussian Subdiffusive Behavior

Abstract

The cellular cytoplasm is a complex, heterogeneous environment (both spatially and temporally) that exhibits viscoelastic behavior. To further develop our quantitative insight into cellular transport, we analyze data sets of mRNA molecules fluorescently labeled with MS2-GFP tracked in real time in live Escherichia coli and Saccharomyces cerevisiae cells. As shown previously, these RNA-protein particles exhibit subdiffusive behavior that is viscoelastic in its origin. Examining the ensemble of particle displacements reveals a Laplace distribution at all observed timescales rather than the Gaussian distribution predicted by the central limit theorem. This ensemble non-Gaussian behavior is caused by a combination of an exponential distribution in the time-averaged diffusivities and non-Gaussian behavior of individual trajectories. We show that the non-Gaussian behavior is a consequence of significant heterogeneity between trajectories and dynamic heterogeneity along single trajectories. Informed by theory and simulation, our work provides an in-depth analysis of the complex diffusive behavior of RNA-protein particles in live cells.

Molecular simulations of cellular processes

“…The favorable inter-crowder interactions improved the calculated diffusion coefficients relative to the approximation of inert crowders. Moreover, the transient associations between crowders allowed to predict a spread of the diffusion coefficients comparable to experimental values Cox 2004, 2006;Lampo et al 2017). This spread, like the average diffusion coefficients, was underestimated in the approximation of inert crowders, consistent with previous findings (Saxton 1997).…”

“…A positive rate of diffusion, termed super-diffusion, often suggests a mechanism of active transport (Reverey et al 2015). A negative rate, so called sub-diffusion, can originate from weak macromolecular associations and increase, for example, the search efficiency of enzymes for their DNA binding sites (Golding and Cox 2006;Guigas and Weiss 2008;Sereshki et al 2012;Lampo et al 2017). The different regimes can alternate on different timescales, reflecting the underlying heterogeneity of and interactions among cellular structures (Di Rienzo et al 2014;van den Berg et al 2017).…”

Model of chromosomal loci dynamics in bacteria as fractional diffusion with intermittent transport

“…Possibly more general sources of heterogeneity than glassyness could also lead to similar effects. Such kind of disorder has been recently implicated for the motion of cytoplasmic particles [12]. A more precise dissection of such hypothesis requires a theoretical approach that incorporates explicitly the more complex physical ingredient of crowding and glassy behavior.…”

“…To this aim, a physical model may be difficult at this stage. The main obstacles are that we still know * marco.cosentino-lagomarsino@upmc.fr very little about both the nature of cytoplasmic diffusion [11,12] and the nature of the nonequilibrium forces driving the chromosome [8,13]. In addition, the contribution of chromosome folding to subdiffusion is an open question [6,14,15].…”

A Bayesian framework for the detection of diffusive heterogeneity

“…While powerful analyses from SPT have indicated the complexity of transport in live 49 cells, the spatial variation of the diffusion coefficient remains poorly characterized. This 50 can be attributed to challenges in disentangling effects of biological heterogeneity and 51 limited sampling of a stochastic process [7,8]. To address these challenges, we developed 52 a Bayesian framework to estimate a posterior distribution of the possible diffusion 53 coefficients underlying single-trajectory dynamics.…”